a. Field of the Invention
The present invention relates to the field of oil recovery system diagnostics and analysis and the human interface for comprehension and affirmative reporting of events associated with the optimization of the oil recovery process.
b. Summary of the Related Art
As the next generation of knowledge-based or artificial intelligence systems begins to emerge, they will be characterized by their ability to deal directly-with the real world environment rather than via a human intermediary on site at a particular location. Among the more interesting classes of problems of this type are those that deal with the interpretation of observations of physical systems. Process monitoring and diagnostic systems are an important subclass. In these systems, a key aspect is to determine when an actual problem has taken place. While outright failures are relatively easy to detect, in many cases more subtle problems may be masked by artifacts of normal operation of the process being studied, its external environment, or the monitoring process itself. In order to detect these problems, it is often necessary to use detectors that are sensitive to a wide range of anomalous behavior and then use domain knowledge to classify the detected events into those that are truly problematical and those that do not require action.
U.S. Pat. No. 5,274,572 describes a method and apparatus for monitoring and analyzing signal data, which uses a network model describing the system under investigation and a runtime agent for acquiring the signal data and accessing the model if an anomaly in the signal data is indicated. The network model describes events of interests and how the events relate to phenomena in the system. The network model is constructed using an object-oriented approach with: observations of the events of interests in the system; situations which describe possible underlying causes of the observations; and relations which specify the logical relationship between the observations and situations. The runtime agent is constructed with an object-oriented approach using observers, which monitor the signal data and compute whether an anomaly in the incoming signal data exists. If an anomaly is identified, an “observation” is generated and the network model entered to analyze the observation and estimate a cause of the observation. The method and apparatus are applicable for interpreting phenomena in a wide variety of physical systems and have been exemplarily applied to monitoring the quality of oil well logging and laboratory material test sensor configurations.
Other known systems provide techniques for interactively analyzing system log-files, which are monitored by technical personnel and systems specialists to determine system performance, status, and software faults, which are often generated during various hardware and software monitoring operations. Each log-file contains time stamped reports. This technique is especially useful for analyzing large log-files. A new release of software may contain many incremental versions that must be tested. The testing of each incremental version may generate a log-file containing thousands of reports. Using this technique, reports are correlated, faults are isolated, and temporal patterns are recognized more quickly and efficiently than by using conventional, non-graphical techniques.
Contemporary oil recovery systems comprise a vast network of various and assorted oilrigs platforms which are typically wide spread geographically. It is complex and moreover prohibitively expensive to physically patrol, inspect and diagnose equipment failures, much less attempt to perform operational optimization in a fleet of hundreds or even thousands of oilrigs comprising a regional or global oil recovery system. Diagnosis and affirmative notification in such a system is complex and thus far in lack of an intelligible human interface. Alarms are typically premature, cryptic or lost in deluge of unintelligible data. Moreover, due to the expense and critical strategic importance of oil recovery systems, there is a critical need for a remote monitoring and diagnostic and notification service for a wide area oil recovery system. There are many types of failures and even a longer list of parameters to monitor in such a system. Thus, there is a need for an automated process running on a plurality of oilrigs comprising an oil recovery system that performs a Health Check monitoring function of an oil recovery system.